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Abstract The Sloan Digital Sky Survey (SDSS) has recently initiated its fifth survey generation (SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V's Milky Way Mapper program will deliver multiepoch optical and near-infrared spectra for more than 5 × 10 6 stars across the entire sky, covering a large range in stellar mass, surface temperature, evolutionary stage, and age. About 10% of those spectra will be of hot stars of OBAF spectral types, for whose analysis no established survey pipelines exist. Here we present the spectral analysis algorithm, ZETA-PAYNE, developed specifically to obtain stellar labels from SDSS-V spectra of stars with these spectral types and drawing on machine-learning tools. We provide details of the algorithm training, its test on artificial spectra, and its validation on two control samples of real stars. Analysis with ZETA-PAYNE leads to only modest internal uncertainties in the near-IR with APOGEE (optical with BOSS): 3%–10% (1%–2%) for T eff , 5%–30% (5%–25%) for v sin i , 1.7–6.3 km s −1 (0.7–2.2 km s −1 ) for radial velocity, <0.1 dex (<0.05 dex) for log g , and 0.4–0.5 dex (0.1 dex) for [M/H] of the star, respectively. We find a good agreement between atmospheric parameters of OBAF-type stars when inferred from their high- and low-resolution optical spectra. For most stellar labels, the APOGEE spectra are (far) less informative than the BOSS spectra of these stars, while log g , v sin i , and [M/H] are in most cases too uncertain for meaningful astrophysical interpretation. This makes BOSS low-resolution optical spectra better for stellar labels of OBAF-type stars, unless the latter are subject to high levels of extinction. 

Abstract PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2R$$_\textrm{Earth}$$ $_{\text{Earth}}^{}$) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.